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Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models

Author

Listed:
  • Habib Akbari-Alashti
  • Omid Bozorg Haddad
  • Miguel Mariño

Abstract

Forecasting flow in rivers has special significance in surface water management, especially in agricultural planning and risk reduction of floods and droughts. In recent years, studies have shown the superiority of forecasting models based on artificial intelligence, using artificial neural networks (ANN) and genetic programming (GP), over time-series models. In this paper, continuous and discrete historical flow records are used for monthly river flow forecasting of the Saeed-Abad river in East Azarbaijan province, Iran. Auto regressive moving average with exogenous inputs (ARMAX), ANN, and GP models are used in both continuous and discrete flow series. For both flow series, results of the ARMAX, ANN, and GP models are then compared and results of each method are evaluated relative to each other. Two quantitative standard statistical performance evaluation measures, coefficient of determination (R 2 ) and root mean square error (RMSE), are employed to evaluate the performance of the aforementioned models. Results show that for the two methods, the GP model is more effective with respect to accuracy than ARMAX and ANN. For continuous time-series forecasting, GP is a more precise model (R 2 = 0.7 and RMSE = 0.172) than either ANN (R 2 = 0.627 and RMSE = 0.193) or ARMAX (R 2 = 0.595 and RMSE = 0.243). For discrete time-series forecasting, the superiority of the GP model is evident in most months. For monthly flow forecasting, results indicate that the discrete time-series forecasting method is superior to the continuous time-series forecasting method. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3211-3225, July.
  • Handle: RePEc:spr:waterr:v:29:y:2015:i:9:p:3211-3225
    DOI: 10.1007/s11269-015-0991-1
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    References listed on IDEAS

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    1. C. Sivapragasam & G. Vasudevan & P. Vincent, 2007. "Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 933-945, June.
    2. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    3. Ashkan Shokri & Omid Bozorg Haddad & Miguel Mariño, 2013. "Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2231-2249, May.
    4. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    5. S. Seifollahi-Aghmiuni & Omid Bozorg Haddad & M. Omid & M. Mariño, 2013. "Effects of Pipe Roughness Uncertainty on Water Distribution Network Performance During its Operational Period," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1581-1599, March.
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    Cited by:

    1. Lan Yu & Soon Keat Tan & Lloyd H. C. Chua, 2017. "Online Ensemble Modeling for Real Time Water Level Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1105-1119, March.

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    Keywords

    ARMAX; ANN; GP; Inflow forecasting;
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